Hierarchical Social Recommendation Model Based on a Graph Neural Network

Bi, Zhongqin; Jing, Lina; Shan, Meijing; Dou, Shuming; Wang, Shiyang

doi:10.1155/2021/9107718

Cited by 7 publications

(3 citation statements)

References 36 publications

(45 reference statements)

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“…These can simply make use of user-item interactions, although they usually also require attributes of both users and items and some of their social information. Among the latter are hybrid proposals that combine GNN with other techniques, such as Long Short-Term Memory (LSTM) [32] or clustering [33]. Other proposals involve different types of social data with additional information [34], including information from multiple sources [35].…”

Section: Related Workmentioning

confidence: 99%

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Sánchez-Moreno,

López Batista,

Muñoz Vicente

et al. 2024

Information

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Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences.

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Section: Related Workmentioning

confidence: 99%

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Sánchez-Moreno,

López Batista,

Muñoz Vicente

et al. 2024

Information

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“…Considering the influence of data sparsity on the learning node feature of graph neural network, Chen et al [29] proposed a model combining a graph neural network and heterogeneous information network to jointly decode multi-source heterogeneous information. Bi et al [30] designed a hierarchical social collaborative filtering framework (GHSCF) based on a GNN-fused bidirectional Long Short-Term Memory (LSTM) network, which effectively captures the information of neighbors and extracts user-item interaction sequences. Zhou et al [31] used user interaction data and movie information to build a three-part graph.…”

Section: Recommendations Based On Graph Neural Networkmentioning

confidence: 99%

A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

Liu

et al. 2023

Electronics

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Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user social information to alleviate this problem. Existing social recommendation methods cannot express the user interaction interest and social influence deeply, which limits the recommendation performance of the system. To address this problem, in this paper we propose a graph neural network social recommendation algorithm integrating multi-head attention mechanism. First, based on the user-item interaction graph and social network graph, the graph neural network is used to learn the high-order relationship between users and items and deeply extract the latent features of users and items. In the process of learning user embedding vector representation based on the social network graph, the multi-head attention mechanism is introduced to increase the importance of friends with high influence. Then, we make rating predictions for the target users according to the learned user embedding vector representation and item embedding vector. The experimental results on the Epinions dataset show that the proposed method outperforms the existing methods in terms of both Recall and Normalized Discounted Cumulative Gain.

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“…Based on the theory of social communication, Bi et al proposed an effective social reference network model. It combines the attention mechanism and bidirectional LSTM in the same frame and uses multilayer perceptrons [6]. In their research, Lee et al proposed an automatic melody extraction algorithm using deep learning.…”

Section: Related Workmentioning

confidence: 99%

Music Recommendation System and Recommendation Model Based on Convolutional Neural Network

Zhang

2022

Mobile Information Systems

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In today’s era of big data with excess information, music is common and everyday, which shows the huge amount of music data. How to obtain one’s favorite music from the massive music database has become a problem, and the emergence of music recommendation systems is also inevitable. In this paper, we take digital piano music as the research object, form comprehensive features using spectrum and notes, design classification methods using convolutional neural networks, and further process the classification results and design recommendation algorithms. The basic method of music recommendation of this algorithm is to determine the structure of the network model, determine the corresponding training model, and improve the parameters on the basis of the typical source network model used in the system experiment. Historical behavior chooses to collect information. Then, it reads the audio data on the system and retrieves it from Mel, which reveals the identity of the music. The classification proposal achieves its goal by denying the similarity between customer preferences and the potential of two musical characteristics. Two recommended methods based on convolutional neural networks are tested in this article. On the whole, the accuracy of the user’s comprehensive feature, recommendation method is higher than the recommendation accuracy rate of the multicategory user. In the comparison experiment of the single-category and multicategory recommendation methods, the average accuracy rate of single-category user feature recommendation is 50.35%; and the recommendation accuracy rate of multicategory user features is higher than the recommendation accuracy rate of single-category user features. The experimental results show that the two recommendation methods can achieve better recommendation results.

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Hierarchical Social Recommendation Model Based on a Graph Neural Network

Cited by 7 publications

References 36 publications

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

Music Recommendation System and Recommendation Model Based on Convolutional Neural Network

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